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A crowd-sourcing approach for translations of minority language user-generated content (UGC)

Dowling, Meghan orcid logoORCID: 0000-0003-1637-4923, Lynn, Teresa and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2017) A crowd-sourcing approach for translations of minority language user-generated content (UGC). In: First workshop on Social Media and User Generated Content Machine Translation, 31 May 2017, Prague, Czech Republic.

Abstract
Data sparsity is a common problem for machine translation of minority and less-resourced languages. While data collection for standard, grammatical text can be challenging enough, efforts for collection of parallel user-generated content can be even more challenging. In this paper we describe an approach to collecting English↔Irish translations of user-generated content (tweets) that overcomes some of these hurdles. We show how a crowd-sourced data collection campaign, which was tailored to our target audience (the Irish language community), proved successful in gathering data for a niche domain. We also discuss the reliability of crowd-sourcing English↔Irish tweet translations in terms of quality by reporting on a self-rating approach along with qualified reviewer ratings.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Minority Languages;
Subjects:Computer Science > Machine translating
Humanities > Irish language
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Copyright Information:© 2017 PBML. Distributed under CC BY-NC-ND
Funders:ADAPT Centre for Digital Content Technology, which is funded under the SFI Research Centres Programme (Grant 13/RC/2016) and is co-funded by the European Regional Development Fund
ID Code:23304
Deposited On:20 May 2019 15:50 by Thomas Murtagh . Last Modified 20 May 2019 15:50
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